AI Strategy for Non-Technical Leaders: Cutting Through the Hype

Every vendor promises AI will transform your business. Every consultant has a roadmap. Every board meeting includes a slide about "AI readiness." But most of what you're hearing is noise.

Here's a framework for thinking about AI that doesn't require a PhD in machine learning—just clear thinking about your business.

The Only Question That Matters#

Before evaluating any AI initiative, ask: What decision or action will this improve, and by how much?

If the answer is vague ("improve efficiency") or unmeasurable ("enhance customer experience"), stop. You don't have an AI opportunity—you have a solution looking for a problem.

The Three Types of AI Value#

Strip away the jargon, and AI creates value in three ways:

1. Automation of Repetitive Tasks#

AI handles tasks that humans currently do manually—data entry, document classification, initial customer inquiries. The value proposition is clear: reduce labor costs, increase throughput, eliminate errors.

Key question: Is the task truly repetitive and rule-based, or does it require judgment that AI can't reliably provide?

2. Augmentation of Human Decision-Making#

AI surfaces information, identifies patterns, or provides recommendations that help humans make better decisions. Think diagnostic assistance, fraud detection alerts, or personalized recommendations.

Key question: Will your people actually use the AI's output, or will they ignore it because they don't trust it?

3. Creation of New Capabilities#

AI enables things that weren't possible before—real-time language translation, generative content creation, predictive maintenance at scale.

Key question: Is this capability actually valuable to your customers, or is it technology for technology's sake?

Red Flags in AI Proposals#

After evaluating dozens of AI initiatives, we've learned to spot trouble early.

"AI-powered" as the main value proposition: If the pitch leads with technology rather than business outcomes, the team doesn't understand your problems.

No clear success metrics: "We'll know it's working when adoption increases" isn't a metric. Neither is "stakeholder satisfaction."

Aggressive timelines: Enterprise AI projects that promise production deployment in 8 weeks are lying. Good projects take 3-6 months minimum.

Black box reluctance: If the vendor can't explain how their AI makes decisions in terms you understand, either they don't know or they're hiding something.

The Data Question Nobody Asks#

Every AI pitch mentions data. Few address the real data questions:

1. Do you actually have the data you think you have? Most enterprises discover their data is messier, more fragmented, and less complete than anyone realized.

2. Is the data accessible? Data locked in legacy systems, governed by complex policies, or spread across silos isn't useful data.

3. Is the data representative? AI trained on historical data perpetuates historical patterns—including biases and outdated practices.

4. Who maintains the data going forward? AI systems degrade when data pipelines break or drift occurs.

Building vs. Buying#

The build-vs-buy decision for AI is more nuanced than for traditional software:

Build when:

• The AI is core to your competitive advantage
• You have the talent (really have it, not "plan to hire")
• Data privacy or regulatory concerns preclude external solutions
• Off-the-shelf solutions genuinely don't fit your needs

Buy when:

• The AI is a utility (email filtering, basic chatbots)
• Time-to-value matters more than customization
• You need to validate the use case before investing heavily
• The vendor has domain expertise you lack

What Good AI Governance Looks Like#

AI governance isn't bureaucracy—it's risk management. Every AI system needs:

• Clear ownership: Someone is accountable for the system's performance and behavior
• Monitoring: Automated detection of performance degradation or drift
• Human override: Ability to intervene when the AI fails
• Audit trail: Documentation of decisions for compliance and learning
• Update process: Mechanism for improving the system over time

The Honest Timeline#

For a meaningful AI initiative in an enterprise context:

Total time from concept to meaningful production value: 9-15 months for most enterprise AI projects.

Anyone promising faster is either solving a much simpler problem or setting you up for disappointment.

Want a realistic assessment of AI opportunities in your organization? Let's talk.